Search Results - Inference from stochastic processes and prediction

Causal inference for multivariate stochastic process prediction by Cabuz, Simona, Abreu, Giuseppe

“…Numerous real world systems of major interest are modeled as sets of analog continuous stochastic processes with delayed and varying causal relationships…”
Get full text

Causal inference for multivariate stochastic process prediction by Simona Maria Cabuz, Giuseppe Abreu

Get full text

Multivariate Stochastic Rayleigh Process: Computational Aspects, Statistical Inference, Estimation and Prediction Analysis by Chakroune, Yassine, El Azri, Abdenbi, Nafidi, Ahmed, Makroz, Ilyasse

“…The main aim of this paper is to introduce a new multivariate stochastic Rayleigh diffusion process as an extension of the univariate stochastic Rayleigh model, which has been the subject of much…”
Get full text

Robust Two-Step Wavelet-Based Inference for Time Series Models by Guerrier, Stéphane, Molinari, Roberto, Victoria-Feser, Maria-Pia, Xu, Haotian

“…Latent time series models such as (the independent sum of) ARMA(p, q) models with additional stochastic processes are increasingly used for data analysis in biology, ecology, engineering, and economics…”
Get full text

An artificial neural network supported stochastic process for degradation modeling and prediction by Liu, Di, Wang, Shaoping

“…•The process parameters are updated by Bayesian inference for online prediction.•Without path information the ANN supported stochastic process is still practical…”
Get full text

Time series : theory and methods by Brockwell, P. J. (Peter J.), Davis, R. A. (Richard A.)

“…This paperback edition is a reprint of the 1991 edition. Time Series: Theory and Methods is a systematic account of linear time series models and their application to the modeling and prediction of data collected sequentially in time…”
Get full text

Multifidelity multilevel Monte Carlo to accelerate approximate Bayesian parameter inference for partially observed stochastic processes by Warne, David J., Prescott, Thomas P., Baker, Ruth E., Simpson, Matthew J.

“…Models of stochastic processes are widely used in almost all fields of science. Theory validation, parameter estimation, and prediction all require model calibration and statistical inference using data…”
Get full text

Bayesian analysis of stochastic process models by Ríos Insua, David, Ruggeri, Fabrizio, Wiper, Michael P.

“… * Looks at inference, prediction and decision making. Researchers, graduate and advanced undergraduate students interested in stochastic processes in fields such as statistics, operations research…”
Get full text

Bayesian LSTM With Stochastic Variational Inference for Estimating Model Uncertainty in Process‐Based Hydrological Models by Li, Dayang, Marshall, Lucy, Liang, Zhongmin, Sharma, Ashish, Zhou, Yan

“… Here, we introduce an alternative to Bayesian MCMC sampling called stochastic variational inference (SVI…”
Get full text

Leapfrog Diffusion Model for Stochastic Trajectory Prediction by Mao, Weibo, Xu, Chenxin, Zhu, Qi, Chen, Siheng, Wang, Yanfeng

“…To model the indeterminacy of human behaviors, stochastic trajectory prediction requires a sophisticated multi-modal distribution of future trajectories…”
Get full text

Inference and prediction for stochastic models of biological populations undergoing migration and proliferation by Simpson, Matthew J, Plank, Michael J

“…Parameter inference is a critical step in the process of interpreting biological data using mathematical models…”
Get more information

Effluent trading planning and its application in water quality management: A factor-interaction perspective by Zhang, J.L., Li, Y.P., Zeng, X.T., Huang, G.H., Li, Y., Zhu, Y., Kong, F.L., Xi, M., Liu, J.

“… Bayesian inference is employed for uncertainty analysis of SWAT model parameters and uncertain prediction of nutrient loadings…”
Get full text

Data-driven method for real-time prediction and uncertainty quantification of fatigue failure under stochastic loading using artificial neural networks and Gaussian process regression by Farid, Maor

“…•The current manuscript focuses on a data-driven method for real-time fatigue failure prediction under stochastic loadings…”
Get full text

Corrections to “On the Nonparametric Prediction of Conditionally Stationary Sequences”, by S. Caires and J.A. Ferreira in Statistical Inference for Stochastic Processes 8 (2005), 151–184

Get full text

Collaborative Online RUL Prediction of Multiple Assets With Analytically Recursive Bayesian Inference by Peng, Weiwen, Chen, Yuan, Xu, Ancha, Ye, Zhi-Sheng

“…) prediction adopt a stochastic process-based degradation model and a computation-intensive parameter estimation method for RUL prediction of a single operating asset…”
Get full text

Deep Bayesian stochastic process model for remaining useful life prediction of rolling bearings by Deng, Minqiang, Xu, Meng, Bian, Wenbin, Liu, Dongying, Deng, Aidong

“…The stochastic process model (SPM) offers a promising approach for remaining useful life (RUL…”
Get full text

Heterogeneous Hypergraph Variational Autoencoder for Link Prediction by Fan, Haoyi, Zhang, Fengbin, Wei, Yuxuan, Li, Zuoyong, Zou, Changqing, Gao, Yue, Dai, Qionghai

“…Link prediction aims at inferring missing links or predicting future ones based on the currently observed network…”
Get full text

High‐Frequency Instruments and Identification‐Robust Inference for Stochastic Volatility Models by Ahsan, Md. Nazmul, Dufour, Jean‐Marie

“… We study parameter inference problems in the proposed framework with nonstationary stochastic volatility and exogenous predictors in the latent volatility process. Identification…”
Get full text

Scalable Semisupervised GMM for Big Data Quality Prediction in Multimode Processes by Yao, Le, Ge, Zhiqiang

“…In this paper, a novel variational inference semisupervised Gaussian mixture model (VI-S 2 GMM…”
Get full text

Amortized Bayesian inference on generative dynamical network models of epilepsy using deep neural density estimators by Hashemi, Meysam, Vattikonda, Anirudh N., Jha, Jayant, Sip, Viktor, Woodman, Marmaduke M., Bartolomei, Fabrice, Jirsa, Viktor K.

“… Such a parametric simulator is equipped with a stochastic generative process, which itself provides the basis for inference and prediction of the local and global brain dynamics affected by disorders…”
Get full text